Sungwook Yoon View Bio
The IT environment is rapidly changing: new technology stacks emerge every year that serves billions of people worldwide. However, many of these new technologies have not been thoroughly tested, and as a result, malware writers have targeted them. How can you quickly and effectively distinguish a network intrusion attempt from an expected and authorized event? A great approach for getting in front of those attacks involves the use of big data technologies for predictive analytics. By analyzing all your network event data with Apache Hadoop and Apache Spark, you can build models that identify normal behavior as well.
Sungwook is a Data Scientist at MapR. Sungwook's data experience includes malware detection algorithms for packet stream analysis, mobile network signaling analysis, social network analysis, job title analysis as well as call center data analysis. Before joining MapR, Sungwook worked as an architect for Seven Networks, a company that delivers device-centric mobile traffic management and analytics for wireless carriers. Previously, Sungwook worked as a Research Scientist at Palo Alto Research Center, where he worked on projects for both DARPA and Xerox. Sungwook's main technical background lies in Artificial Intelligence and Machine Learning. His Artificial Intelligence reserach has been published in top-tier conferences and journals, including AAAI, ICAPS, NIPS, UAI, ICML, JAIR, and JMLR.
Sungwook holds a Ph.D. in Computer Engineering from Purdue University,and M.S.and B.S. degrees in Electrical Engineering from Seoul National University.